The use of multimedia technology and gamification has offered an alternative way of delivering information in education. Interactive Multimedia has the potential to revolutionize the way we work, learn and communicate. With gamification and interactive multimedia, the learning process becomes active, not passive and it ensures that users are doing, not simply watching. Also the Laboratory has a great role in enhancing students' skills as it is a vital environment of a variety of activities and experiments in which science is delivered. In this paper we introduce our concept of designing a

Tracking by detection methods are becoming increasingly popular in recent years. They use samples classified in previous frames to detect object in a new frame. These methods have shown successful results. However, due to the self updating nature of this approach, tracking by detection methods usually suffer from object drift. Inaccurately detected samples are added to the training set which degrades the performance. Another problem is that the object may change in shape and size which increases the potential for inaccurate detection and subsequently the chance of losing the object. We propose

It is an undeniable fact that computer science has gone a long way, from the invention of transistor-based electronic computers to the rise of artificial intelligence and quantum computing. Now that we are looking at the quantum horizon, the last piece that would complete the quantum revolution is quantum networking. © 2020 IEEE.

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder's feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full

Detection of pedestrians on embedded devices, such as those on-board of robots and drones, has many applications including road intersection monitoring, security, crowd monitoring and surveillance, to name a few. However, the problem can be challenging due to continuously-changing camera viewpoint and varying object appearances as well as the need for lightweight algorithms suitable for embedded systems. This paper proposes a robust framework for pedestrian detection in many footages. The framework performs fine and coarse detections on different image regions and exploits temporal and spatial

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational

Recommender systems are receiving great attention these days, as various researchers and major companies are conducting continuous research in this field. Companies like Google and Amazon have provided different effective models for video recommendation systems, but the educational field is poorly studied as other researchers explained. Different researchers proposed various approaches showing the challenges related to recommender systems and have proposed various effective recommender systems. This paper aims to propose a hybrid recommender framework that can recommend educational courses'

Taxonomic assignment is the core of targeted metagenomics approaches that aims to assign sequencing reads to their corresponding taxonomy. Sequence similarity searching and machine learning (ML) are two commonly used approaches for taxonomic assignment based on the 16S rRNA. Similarity based approaches require high computation resources, while ML approaches dont need these resources in prediction. The majority of these ML approaches depend on k-mer frequency rather than direct sequence, which leads to low accuracy on short reads as k-mer frequency doesnt consider k-mer position. Moreover

Metagenomics is a rapidly emerging field that is concerned with the study of microbial communities 'microbiomes' on both levels of taxonomic classification and functional annotation. Targeted amplicon (16S rRNA) and whole genome shotgun (WGS) sequencing are the two main sequencing strategies in metagenomics. As amplicon sequencing provides a cheap way to classify the composition of a microbial community, it lacks the ability to identify microbial genes and annotate its corresponding functions. On the other hand, WGS sequencing allows further investigation of the complete genomes with all

Pediatric septic shock is generally considered as a devastating clinical syndrome that can lead to tissue damage and organ failure due to the over exaggerated immune response to an infection. Therefore, in this paper, we attempted to early identify the clinical course of such disease with the aid of peripheral blood T-cells of 181 pediatric patients who admitted to Intensive Care Unit (ICU), Accordingly, 34 differential expressed genes have been identified as biological genetic biomarkers. Minimum redundancy and maximum relevance feature selection strategy has been proposed for the discovery